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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.15

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/mag analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2024-06-24, 13:45 CEST based on data in: /cephyr/NOBACKUP/groups/jbp/matev/OceanARM/Lakes/work/fc/c74d3ec2604a7cc87a6a969adf9449


        General Statistics

        Showing 19958 samples.

        loading..

        FastQC: raw reads

        FastQC: raw reads is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        314 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.

        Filtered Reads

        Filtering statistics of sampled reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Insert Sizes

        Insert size estimation of sampled reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality

        Average sequencing quality over each base of all reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        GC Content

        Average GC content over each base of all reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        N content

        Average N content over each base of all reads.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        FastQC: after preprocessing

        FastQC: after preprocessing After trimming and, if requested, contamination removal.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        314 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        loading..

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        Bowtie2: PhiX removal

        Mapping statistics of reads mapped against PhiX and subsequently removed.DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4.

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Bowtie2: assembly

        Mapping statistics of reads mapped against assemblies.DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4.

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        BUSCO

        BUSCO assesses genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs. In case BUSCO's automated lineage selection was used, only generic results for the selected domain are shown and only for genome bins and kept, unbinned contigs for which the BUSCO analysis was successfull, i.e. not for contigs for which no BUSCO genes could be found. Bins for which a specific virus lineage was selected are also not shown.DOI: 10.1093/bioinformatics/btv351.

        Lineage: bacteria_odb10

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        QUAST: assembly

        QUAST: assembly Assembly statistics of raw assemblies.DOI: 10.1093/bioinformatics/btt086.

        Assembly Statistics

        Showing 157/157 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-A1
        1.5Kbp
        0.7Kbp
        36.6K
        116.1K
        202.6Kbp
        317.2Mbp
        MEGAHIT-A2
        1.0Kbp
        0.6Kbp
        164.9K
        382.7K
        417.7Kbp
        667.1Mbp
        MEGAHIT-A3
        1.0Kbp
        0.6Kbp
        105.3K
        265.3K
        463.3Kbp
        494.9Mbp
        MEGAHIT-A4
        1.0Kbp
        0.7Kbp
        90.5K
        232.0K
        455.0Kbp
        445.5Mbp
        MEGAHIT-AM-2014-D1
        1.9Kbp
        0.9Kbp
        32.0K
        108.0K
        573.6Kbp
        379.8Mbp
        MEGAHIT-AM-2014-D2
        1.8Kbp
        0.8Kbp
        36.9K
        120.5K
        772.8Kbp
        384.8Mbp
        MEGAHIT-AM-2014-D3
        1.7Kbp
        0.8Kbp
        38.9K
        120.8K
        404.9Kbp
        363.5Mbp
        MEGAHIT-AM-2014-D4
        1.7Kbp
        0.8Kbp
        35.6K
        109.4K
        404.9Kbp
        327.8Mbp
        MEGAHIT-AM-2014-D5
        1.2Kbp
        0.7Kbp
        54.6K
        146.5K
        354.8Kbp
        335.0Mbp
        MEGAHIT-AM-2014-D6
        1.4Kbp
        0.8Kbp
        65.4K
        180.5K
        323.0Kbp
        450.0Mbp
        MEGAHIT-AM-2014-D7
        1.4Kbp
        0.8Kbp
        73.3K
        202.2K
        344.9Kbp
        509.4Mbp
        MEGAHIT-AM-2014-D8
        1.6Kbp
        0.8Kbp
        86.7K
        265.7K
        371.2Kbp
        759.7Mbp
        MEGAHIT-AM1
        1.5Kbp
        0.8Kbp
        38.9K
        118.7K
        511.4Kbp
        321.2Mbp
        MEGAHIT-AM2
        1.5Kbp
        0.8Kbp
        58.4K
        165.5K
        658.8Kbp
        443.7Mbp
        MEGAHIT-AM3
        1.6Kbp
        0.8Kbp
        45.9K
        158.7K
        404.9Kbp
        478.7Mbp
        MEGAHIT-AM4
        1.4Kbp
        0.8Kbp
        43.3K
        130.5K
        333.7Kbp
        340.4Mbp
        MEGAHIT-AM5
        1.5Kbp
        0.8Kbp
        92.0K
        270.9K
        323.0Kbp
        752.1Mbp
        MEGAHIT-C2
        1.2Kbp
        0.7Kbp
        79.1K
        186.3K
        345.8Kbp
        402.9Mbp
        MEGAHIT-C3
        0.9Kbp
        0.6Kbp
        73.6K
        167.8K
        571.9Kbp
        285.7Mbp
        MEGAHIT-C4
        0.9Kbp
        0.6Kbp
        96.2K
        212.5K
        571.8Kbp
        333.2Mbp
        MEGAHIT-D1
        0.8Kbp
        0.6Kbp
        64.4K
        142.6K
        168.0Kbp
        219.6Mbp
        MEGAHIT-D2
        0.8Kbp
        0.6Kbp
        64.1K
        139.9K
        180.8Kbp
        213.5Mbp
        MEGAHIT-D3
        0.8Kbp
        0.6Kbp
        54.6K
        119.9K
        262.2Kbp
        179.8Mbp
        MEGAHIT-D4
        0.9Kbp
        0.6Kbp
        40.9K
        103.5K
        295.7Kbp
        187.2Mbp
        MEGAHIT-Day2-1
        1.2Kbp
        0.7Kbp
        53.3K
        140.6K
        237.4Kbp
        318.4Mbp
        MEGAHIT-Day2-2
        1.8Kbp
        0.8Kbp
        36.4K
        127.3K
        575.2Kbp
        418.0Mbp
        MEGAHIT-Day2-3
        1.8Kbp
        0.8Kbp
        30.3K
        101.6K
        438.2Kbp
        318.8Mbp
        MEGAHIT-Day2-4
        1.7Kbp
        0.8Kbp
        35.0K
        107.4K
        571.8Kbp
        321.9Mbp
        MEGAHIT-Day2-5
        1.5Kbp
        0.8Kbp
        38.4K
        112.9K
        522.9Kbp
        311.1Mbp
        MEGAHIT-Day2-6
        1.5Kbp
        0.7Kbp
        46.7K
        137.1K
        892.5Kbp
        362.0Mbp
        MEGAHIT-E1
        1.5Kbp
        0.8Kbp
        26.7K
        79.1K
        383.1Kbp
        211.4Mbp
        MEGAHIT-E2
        1.4Kbp
        0.7Kbp
        31.4K
        91.9K
        424.6Kbp
        234.5Mbp
        MEGAHIT-E3
        0.9Kbp
        0.6Kbp
        101.3K
        234.1K
        130.1Kbp
        399.5Mbp
        MEGAHIT-E4
        1.0Kbp
        0.7Kbp
        32.2K
        90.1K
        256.3Kbp
        184.9Mbp
        MEGAHIT-E5
        1.0Kbp
        0.6Kbp
        31.2K
        82.9K
        212.1Kbp
        156.0Mbp
        MEGAHIT-Erken-D1
        1.3Kbp
        0.7Kbp
        88.0K
        225.6K
        208.4Kbp
        518.8Mbp
        MEGAHIT-Erken-D10
        1.2Kbp
        0.7Kbp
        109.0K
        271.2K
        239.4Kbp
        567.1Mbp
        MEGAHIT-Erken-D11
        1.1Kbp
        0.7Kbp
        140.5K
        346.5K
        269.9Kbp
        718.8Mbp
        MEGAHIT-Erken-D2
        1.3Kbp
        0.8Kbp
        103.6K
        270.7K
        202.3Kbp
        654.5Mbp
        MEGAHIT-Erken-D3
        1.4Kbp
        0.8Kbp
        106.3K
        293.0K
        250.5Kbp
        743.6Mbp
        MEGAHIT-Erken-D4
        1.4Kbp
        0.8Kbp
        136.8K
        361.3K
        247.3Kbp
        885.1Mbp
        MEGAHIT-Erken-D5
        1.2Kbp
        0.7Kbp
        134.7K
        330.7K
        482.3Kbp
        712.7Mbp
        MEGAHIT-Erken-D6
        1.2Kbp
        0.7Kbp
        115.0K
        279.1K
        246.4Kbp
        585.4Mbp
        MEGAHIT-Erken-D7
        1.2Kbp
        0.7Kbp
        118.1K
        291.1K
        317.2Kbp
        625.7Mbp
        MEGAHIT-Erken-D8
        1.2Kbp
        0.7Kbp
        112.0K
        278.4K
        345.0Kbp
        595.7Mbp
        MEGAHIT-Erken-D9
        1.2Kbp
        0.7Kbp
        97.3K
        244.5K
        240.7Kbp
        526.1Mbp
        MEGAHIT-F1
        1.2Kbp
        0.7Kbp
        37.6K
        102.4K
        252.3Kbp
        233.4Mbp
        MEGAHIT-F3
        1.0Kbp
        0.6Kbp
        62.4K
        150.7K
        138.3Kbp
        273.5Mbp
        MEGAHIT-F4
        0.9Kbp
        0.6Kbp
        65.4K
        154.5K
        298.9Kbp
        264.4Mbp
        MEGAHIT-KR1
        1.0Kbp
        0.7Kbp
        82.1K
        201.9K
        658.4Kbp
        394.6Mbp
        MEGAHIT-KR2
        1.4Kbp
        0.7Kbp
        46.2K
        142.7K
        402.3Kbp
        380.3Mbp
        MEGAHIT-KR3
        1.5Kbp
        0.8Kbp
        51.7K
        155.6K
        516.7Kbp
        416.3Mbp
        MEGAHIT-KR4
        1.3Kbp
        0.7Kbp
        87.8K
        222.9K
        308.4Kbp
        503.1Mbp
        MEGAHIT-KR5
        1.0Kbp
        0.7Kbp
        114.4K
        258.5K
        234.5Kbp
        476.9Mbp
        MEGAHIT-KT1
        1.2Kbp
        0.7Kbp
        62.9K
        167.5K
        416.5Kbp
        372.7Mbp
        MEGAHIT-KT2
        1.1Kbp
        0.7Kbp
        87.7K
        222.9K
        410.2Kbp
        457.4Mbp
        MEGAHIT-KT3
        1.3Kbp
        0.7Kbp
        58.6K
        160.4K
        328.4Kbp
        373.0Mbp
        MEGAHIT-KT4
        1.3Kbp
        0.7Kbp
        91.2K
        238.6K
        328.5Kbp
        552.6Mbp
        MEGAHIT-KT5
        1.5Kbp
        0.8Kbp
        45.3K
        126.1K
        177.2Kbp
        332.3Mbp
        MEGAHIT-KT6
        1.2Kbp
        0.7Kbp
        71.6K
        187.7K
        328.2Kbp
        405.6Mbp
        MEGAHIT-Ki1-0-7m
        1.8Kbp
        0.8Kbp
        28.0K
        101.1K
        273.7Kbp
        324.4Mbp
        MEGAHIT-Ki1-1-6m
        2.3Kbp
        0.9Kbp
        36.1K
        124.6K
        401.3Kbp
        479.1Mbp
        MEGAHIT-Ki1-2-2m
        1.2Kbp
        0.7Kbp
        28.7K
        84.8K
        299.4Kbp
        202.7Mbp
        MEGAHIT-Ki1-2-7m
        1.8Kbp
        0.8Kbp
        41.8K
        139.7K
        252.2Kbp
        444.9Mbp
        MEGAHIT-Ki2-D1
        1.9Kbp
        0.9Kbp
        54.0K
        161.7K
        643.1Kbp
        542.3Mbp
        MEGAHIT-Ki2-D2
        2.1Kbp
        0.9Kbp
        56.2K
        174.0K
        457.5Kbp
        634.3Mbp
        MEGAHIT-Ki2-D3
        2.0Kbp
        0.9Kbp
        50.3K
        176.4K
        553.0Kbp
        630.4Mbp
        MEGAHIT-Ki2-D4
        1.3Kbp
        0.7Kbp
        91.6K
        245.7K
        418.3Kbp
        580.8Mbp
        MEGAHIT-Ki3-D1
        1.2Kbp
        0.7Kbp
        101.7K
        258.7K
        363.0Kbp
        562.8Mbp
        MEGAHIT-Ki3-D2
        1.9Kbp
        0.9Kbp
        64.4K
        215.1K
        466.7Kbp
        729.2Mbp
        MEGAHIT-Ki3-D3
        1.8Kbp
        0.8Kbp
        46.7K
        148.7K
        413.7Kbp
        471.6Mbp
        MEGAHIT-Ki3-D4
        1.7Kbp
        0.8Kbp
        41.8K
        129.1K
        413.3Kbp
        398.2Mbp
        MEGAHIT-LJ-13-9m
        1.2Kbp
        0.7Kbp
        109.1K
        278.7K
        215.6Kbp
        606.8Mbp
        MEGAHIT-LJ-3-15m
        1.0Kbp
        0.7Kbp
        117.1K
        276.9K
        238.7Kbp
        494.6Mbp
        MEGAHIT-LJ-3-4m
        1.5Kbp
        0.8Kbp
        64.9K
        194.2K
        241.0Kbp
        522.1Mbp
        MEGAHIT-LJ-3-65m
        1.7Kbp
        0.8Kbp
        71.3K
        221.5K
        241.0Kbp
        677.7Mbp
        MEGAHIT-LJ-3-9m
        1.2Kbp
        0.7Kbp
        46.9K
        123.3K
        139.6Kbp
        276.6Mbp
        MEGAHIT-LJ-5-9m
        2.0Kbp
        0.9Kbp
        63.7K
        221.4K
        348.0Kbp
        777.4Mbp
        MEGAHIT-LJ-7-9m
        2.1Kbp
        0.9Kbp
        60.1K
        197.7K
        285.6Kbp
        701.0Mbp
        MEGAHIT-LaPlata-15m-a
        1.5Kbp
        0.8Kbp
        60.1K
        176.5K
        307.6Kbp
        487.6Mbp
        MEGAHIT-LaPlata-1m-a
        1.4Kbp
        0.7Kbp
        49.1K
        145.3K
        237.6Kbp
        376.6Mbp
        MEGAHIT-LaPlata-5m-a
        1.5Kbp
        0.8Kbp
        54.9K
        159.0K
        202.1Kbp
        418.6Mbp
        MEGAHIT-LaPlata-7m-a
        1.6Kbp
        0.8Kbp
        42.8K
        134.1K
        579.8Kbp
        396.1Mbp
        MEGAHIT-Loc080925_4_5m
        1.2Kbp
        0.7Kbp
        106.4K
        269.5K
        387.3Kbp
        595.4Mbp
        MEGAHIT-Loc080925_4m
        1.2Kbp
        0.7Kbp
        118.9K
        292.2K
        387.5Kbp
        619.8Mbp
        MEGAHIT-Loc080925_5_5m
        1.7Kbp
        0.8Kbp
        53.0K
        162.0K
        427.0Kbp
        485.8Mbp
        MEGAHIT-Loc080925_5m
        1.2Kbp
        0.7Kbp
        130.1K
        323.0K
        427.0Kbp
        685.7Mbp
        MEGAHIT-Loc080925_6m
        1.6Kbp
        0.8Kbp
        82.3K
        242.8K
        363.7Kbp
        686.1Mbp
        MEGAHIT-Loc080925_7m
        1.7Kbp
        0.8Kbp
        70.4K
        219.7K
        392.5Kbp
        655.8Mbp
        MEGAHIT-Loc080925_8_8m
        1.5Kbp
        0.8Kbp
        68.0K
        186.9K
        427.0Kbp
        502.6Mbp
        MEGAHIT-Loc080925_8m
        1.6Kbp
        0.8Kbp
        70.4K
        213.1K
        427.0Kbp
        613.5Mbp
        MEGAHIT-Loc081215_1m
        1.6Kbp
        0.8Kbp
        71.2K
        199.0K
        302.9Kbp
        553.2Mbp
        MEGAHIT-Loc081215_4m
        1.6Kbp
        0.8Kbp
        63.7K
        183.5K
        375.8Kbp
        530.7Mbp
        MEGAHIT-Loc081215_5m
        1.5Kbp
        0.8Kbp
        79.5K
        217.4K
        326.3Kbp
        580.5Mbp
        MEGAHIT-Loc081215_6m
        1.5Kbp
        0.8Kbp
        81.8K
        231.5K
        446.4Kbp
        638.2Mbp
        MEGAHIT-Loc081215_8m
        1.5Kbp
        0.8Kbp
        104.4K
        298.3K
        261.5Kbp
        814.5Mbp
        MEGAHIT-Loc081215_9m
        1.4Kbp
        0.8Kbp
        103.0K
        274.3K
        363.2Kbp
        682.4Mbp
        MEGAHIT-Loc090116_1m
        1.1Kbp
        0.7Kbp
        154.8K
        380.8K
        364.3Kbp
        764.5Mbp
        MEGAHIT-Loc090116_4m
        1.3Kbp
        0.7Kbp
        123.4K
        318.4K
        365.4Kbp
        745.5Mbp
        MEGAHIT-Loc090116_7m
        1.3Kbp
        0.7Kbp
        112.7K
        297.6K
        539.7Kbp
        712.2Mbp
        MEGAHIT-Loc090116_8m
        1.4Kbp
        0.8Kbp
        116.3K
        314.8K
        490.2Kbp
        785.1Mbp
        MEGAHIT-Loc090116_9m
        1.2Kbp
        0.7Kbp
        114.2K
        288.6K
        238.3Kbp
        628.1Mbp
        MEGAHIT-Loc090402_1m
        1.0Kbp
        0.6Kbp
        157.2K
        366.9K
        209.7Kbp
        644.9Mbp
        MEGAHIT-Loc090402_4m
        1.2Kbp
        0.7Kbp
        134.6K
        344.7K
        210.2Kbp
        727.5Mbp
        MEGAHIT-Loc090402_7m
        1.3Kbp
        0.7Kbp
        112.9K
        291.0K
        367.0Kbp
        679.8Mbp
        MEGAHIT-Loc090402_8_9m
        1.0Kbp
        0.7Kbp
        115.1K
        268.6K
        362.8Kbp
        505.9Mbp
        MEGAHIT-Loc090402_8m
        1.3Kbp
        0.7Kbp
        115.2K
        298.4K
        364.9Kbp
        705.5Mbp
        MEGAHIT-Loc090519_1m
        1.5Kbp
        0.8Kbp
        80.0K
        225.4K
        461.4Kbp
        612.8Mbp
        MEGAHIT-Loc090519_4m
        1.4Kbp
        0.8Kbp
        57.5K
        165.4K
        363.6Kbp
        435.9Mbp
        MEGAHIT-Loc090519_5m
        1.6Kbp
        0.8Kbp
        67.3K
        198.6K
        440.4Kbp
        583.6Mbp
        MEGAHIT-Loc090519_6m
        1.7Kbp
        0.8Kbp
        57.3K
        174.8K
        440.4Kbp
        529.6Mbp
        MEGAHIT-Loc090519_7m
        1.8Kbp
        0.8Kbp
        68.8K
        209.8K
        440.4Kbp
        657.4Mbp
        MEGAHIT-Loc090519_8_7m
        1.4Kbp
        0.8Kbp
        104.4K
        286.2K
        364.2Kbp
        731.4Mbp
        MEGAHIT-Loc090519_8m
        1.5Kbp
        0.8Kbp
        80.9K
        236.0K
        231.6Kbp
        644.8Mbp
        MEGAHIT-Loc090721_1m
        1.1Kbp
        0.7Kbp
        83.2K
        201.3K
        222.8Kbp
        386.2Mbp
        MEGAHIT-Loc090721_4m
        1.3Kbp
        0.7Kbp
        89.7K
        232.2K
        513.3Kbp
        531.2Mbp
        MEGAHIT-Loc090721_5m
        1.3Kbp
        0.8Kbp
        62.2K
        160.8K
        401.8Kbp
        380.9Mbp
        MEGAHIT-Loc090721_6m
        1.3Kbp
        0.7Kbp
        72.5K
        200.3K
        427.2Kbp
        495.0Mbp
        MEGAHIT-Loc090721_7m
        1.3Kbp
        0.7Kbp
        86.6K
        234.0K
        426.9Kbp
        563.7Mbp
        MEGAHIT-Loc090721_8_6m
        1.4Kbp
        0.8Kbp
        100.2K
        277.0K
        436.8Kbp
        708.4Mbp
        MEGAHIT-Loc090721_8m
        1.4Kbp
        0.8Kbp
        104.0K
        278.2K
        307.5Kbp
        683.2Mbp
        MEGAHIT-Loc090907_1m
        1.1Kbp
        0.7Kbp
        110.5K
        268.9K
        396.6Kbp
        526.1Mbp
        MEGAHIT-Loc090907_4m
        1.0Kbp
        0.7Kbp
        117.3K
        272.7K
        281.3Kbp
        493.5Mbp
        MEGAHIT-Loc090907_5m
        1.5Kbp
        0.7Kbp
        56.8K
        165.9K
        363.7Kbp
        432.0Mbp
        MEGAHIT-Loc090907_6m
        1.3Kbp
        0.7Kbp
        69.9K
        190.6K
        427.0Kbp
        459.3Mbp
        MEGAHIT-Loc090907_8_6m
        1.5Kbp
        0.8Kbp
        77.0K
        219.0K
        392.4Kbp
        584.0Mbp
        MEGAHIT-Loc090907_8m
        1.4Kbp
        0.8Kbp
        85.6K
        234.4K
        392.4Kbp
        590.6Mbp
        MEGAHIT-MJ-2014-D1
        2.8Kbp
        0.9Kbp
        13.8K
        59.3K
        310.2Kbp
        268.5Mbp
        MEGAHIT-MJ-2014-D2
        2.8Kbp
        1.0Kbp
        16.2K
        64.8K
        381.3Kbp
        299.1Mbp
        MEGAHIT-MJ-2014-D3
        2.5Kbp
        0.9Kbp
        15.0K
        59.6K
        355.0Kbp
        247.5Mbp
        MEGAHIT-MJ-2014-D4
        2.0Kbp
        0.8Kbp
        20.2K
        77.9K
        419.8Kbp
        271.9Mbp
        MEGAHIT-MJ-2014-D5
        1.9Kbp
        0.8Kbp
        12.2K
        49.3K
        377.2Kbp
        166.1Mbp
        MEGAHIT-MJ-2014-D6
        1.1Kbp
        0.7Kbp
        40.3K
        107.0K
        276.9Kbp
        227.4Mbp
        MEGAHIT-MJ-2014-D7
        1.2Kbp
        0.7Kbp
        34.2K
        96.2K
        280.3Kbp
        216.5Mbp
        MEGAHIT-MJ1
        1.7Kbp
        0.8Kbp
        26.1K
        88.0K
        254.9Kbp
        267.0Mbp
        MEGAHIT-MJ2
        2.2Kbp
        0.9Kbp
        8.9K
        29.8K
        218.8Kbp
        110.9Mbp
        MEGAHIT-MJ3
        2.5Kbp
        0.9Kbp
        4.6K
        19.9K
        306.4Kbp
        82.3Mbp
        MEGAHIT-MJ4
        1.3Kbp
        0.7Kbp
        17.8K
        58.3K
        224.9Kbp
        144.1Mbp
        MEGAHIT-Umea1p1
        1.2Kbp
        0.7Kbp
        82.7K
        215.3K
        238.2Kbp
        466.4Mbp
        MEGAHIT-Umea1p2
        1.6Kbp
        0.8Kbp
        59.0K
        187.7K
        491.1Kbp
        556.5Mbp
        MEGAHIT-Umea1p3
        2.2Kbp
        0.9Kbp
        39.9K
        143.9K
        627.9Kbp
        534.8Mbp
        MEGAHIT-Umea1p4
        1.3Kbp
        0.7Kbp
        56.9K
        159.8K
        426.2Kbp
        388.2Mbp
        MEGAHIT-Umea2p1
        2.4Kbp
        0.9Kbp
        31.1K
        123.8K
        586.7Kbp
        510.9Mbp
        MEGAHIT-Umea2p2
        2.3Kbp
        0.9Kbp
        30.9K
        114.3K
        416.9Kbp
        456.8Mbp
        MEGAHIT-Umea2p3
        2.0Kbp
        0.9Kbp
        43.8K
        166.1K
        470.1Kbp
        603.1Mbp
        MEGAHIT-Umea3p1
        2.0Kbp
        0.8Kbp
        30.5K
        118.2K
        555.7Kbp
        418.3Mbp
        MEGAHIT-Umea3p2
        2.3Kbp
        1.0Kbp
        44.0K
        154.6K
        821.9Kbp
        630.2Mbp
        MEGAHIT-Umea3p3
        2.0Kbp
        0.9Kbp
        40.1K
        145.8K
        732.7Kbp
        532.8Mbp
        MEGAHIT-Umea3p4
        1.9Kbp
        0.9Kbp
        63.0K
        203.8K
        292.1Kbp
        684.1Mbp
        MEGAHIT-VK1
        1.2Kbp
        0.7Kbp
        69.1K
        204.2K
        436.4Kbp
        469.2Mbp
        MEGAHIT-VK2
        1.5Kbp
        0.8Kbp
        63.1K
        183.5K
        393.0Kbp
        498.8Mbp
        MEGAHIT-VK3
        1.9Kbp
        0.8Kbp
        54.6K
        178.7K
        616.3Kbp
        594.9Mbp
        MEGAHIT-VK4
        1.5Kbp
        0.8Kbp
        50.8K
        162.6K
        466.6Kbp
        464.9Mbp
        MEGAHIT-YR1
        1.2Kbp
        0.7Kbp
        80.5K
        204.7K
        658.5Kbp
        455.6Mbp
        MEGAHIT-YR2
        2.1Kbp
        0.9Kbp
        31.2K
        109.8K
        658.8Kbp
        399.7Mbp
        MEGAHIT-YR3
        1.4Kbp
        0.7Kbp
        52.5K
        162.1K
        349.1Kbp
        428.1Mbp
        MEGAHIT-YR4
        1.7Kbp
        0.8Kbp
        45.9K
        132.9K
        275.1Kbp
        394.2Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        QUAST: bins

        QUAST: bins Assembly statistics of binned assemblies.DOI: 10.1093/bioinformatics/btt086.

        Assembly Statistics

        Showing 16374 samples.

        loading..

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Prokka

        Prokka is a software tool for the rapid annotation of prokaryotic genomes.DOI: 10.1093/bioinformatics/btu153.

        This barplot shows the distribution of different types of features found in each contig.

        Prokka can detect different features:

        • CDS
        • rRNA
        • tmRNA
        • tRNA
        • miscRNA
        • signal peptides
        • CRISPR arrays

        This barplot shows you the distribution of these different types of features found in each contig.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        nf-core/mag Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using nf-core/mag v2.5.1 ((doi: 10.1093/nargab/lqac007); Krakau et al., 2022) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v23.10.0 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/mag -profile apptainer --input Samplesheet_input2024.csv --outdir result_LakesARGs --gtdb_db 'https://data.ace.uq.edu.au/public/gtdb/data/releases/release214/214.1/auxillary_files/gtdbtk_r214_data.tar.gz' --skip_spades --skip_spadeshybrid --skip_maxbin2 --skip_concoct --max_time 30.h --gtdbtk_min_completeness 40.0 --run_virus_identification --busco_db 'https://busco-data.ezlab.org/v5/data/lineages/bacteria_odb10.2024-01-08.tar.gz' --save_busco_db --busco_clean --genomad_db genomad_db -c config_time.nf -resume tender_banach

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1). https://doi.org/10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/mag Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        BOWTIE2_ASSEMBLY_ALIGN bowtie2 2.4.2
        pigz 2.3.4
        samtools 1.11
        BOWTIE2_PHIX_REMOVAL_ALIGN bowtie2 2.4.2
        BUSCO R 4.1.3
        busco 5.4.3
        python 3.9.13
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.11.4
        yaml 6.0
        FASTP fastp 0.23.4
        FASTQC_RAW fastqc 0.11.9
        FASTQC_TRIMMED fastqc 0.11.9
        GTDBTK_CLASSIFYWF gtdbtk 2.3.2
        GUNZIP_BINS gunzip 1.10
        MAG_DEPTHS pandas 1.1.5
        python 3.6.7
        MAG_DEPTHS_PLOT pandas 1.3.0
        python 3.9.6
        seaborn 0.11.0
        MAG_DEPTHS_SUMMARY pandas 1.4.3
        python 3.10.6
        MEGAHIT megahit 1.2.9
        METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS metabat2 2.15
        METABAT2_METABAT2 metabat2 2.15
        PRODIGAL pigz 2.6
        prodigal 2.6.3
        PROKKA prokka 1.14.6
        QUAST metaquast 5.0.2
        python 3.7.6
        QUAST_BINS metaquast 5.0.2
        python 3.7.6
        SPLIT_FASTA biopython 1.7.4
        pandas 1.1.5
        python 3.6.7
        Workflow Nextflow 23.10.0
        nf-core/mag 2.5.1

        nf-core/mag Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        revision
        master
        runName
        distracted_almeida
        containerEngine
        apptainer
        launchDir
        /cephyr/NOBACKUP/groups/jbp/matev/OceanARM/Lakes
        workDir
        /cephyr/NOBACKUP/groups/jbp/matev/OceanARM/Lakes/work
        projectDir
        /cephyr/users/matev/Vera/.nextflow/assets/nf-core/mag
        userName
        matev
        profile
        apptainer
        configFiles
        N/A

        Input/output options

        input
        Samplesheet_input2024.csv
        outdir
        result_LakesARGs

        Max job request options

        max_time
        30.h

        Quality control for short reads options

        phix_reference
        /cephyr/users/matev/Vera/.nextflow/assets/nf-core/mag/assets/data/GCA_002596845.1_ASM259684v1_genomic.fna.gz

        Quality control for long reads options

        lambda_reference
        /cephyr/users/matev/Vera/.nextflow/assets/nf-core/mag/assets/data/GCA_000840245.1_ViralProj14204_genomic.fna.gz

        Taxonomic profiling options

        gtdbtk_min_completeness
        40.0
        gtdbtk_min_perc_aa
        10
        gtdbtk_pplacer_cpus
        1
        genomad_db
        genomad_db

        Assembly options

        skip_spades
        true
        skip_spadeshybrid
        true

        Virus identification options

        run_virus_identification
        true

        Binning options

        skip_maxbin2
        true
        skip_concoct
        true

        Bin quality check options

        busco_db
        https://busco-data.ezlab.org/v5/data/lineages/bacteria_odb10.2024-01-08.tar.gz
        save_busco_db
        true
        busco_clean
        true